In a study done in 2002, nearly one of six African Americans (15%), one of seven Hispanics (13%), and one of ten Asian Americans (11%) reported that they would receive better healthcare if they were of a different race or ethnicity compared with 1% of whites.

During the same study, one in five Hispanics (22%), one of six African-Americans (17%), and one of six Asian Americans (17%) rate their health as fair or poor compared with one of seven whites (14%).

In a separate study done in 2020, one in five women reported that they felt a health care provider has ignored or dismissed their symptoms and 17% of women stated they feel they have been treated differently because of their gender compared with 14% and 6% of men.

LGBTQ+ individuals are more than twice as likely as heterosexual men and women to have a mental health disorder in their lifetime.

The model is based on the fact that within the United States, it has been proven that certain factors such as race/ethnicity and gender/sexual orientation show measurable differences in regards to access to healthcare, the presence of disease, and health outcomes.

Disparities within the healthcare system have been a growing issue, especially when hundreds of years ago, the Federal Healthcare System was created.

There have been a myriad of studies (such as the CDC, US Department Of Health and Human Services, and a landmark study called the Malone Heckler Report) that show that the issue regarding disparities within healthcare will only worsen if this topic is not addressed.

There have been a plethora of studies that discuss the same concept of what our model is based off of and some studies provided several aspects that need to be addressed in order for there to be positive and effective change in healthcare regarding minorities.

Those aspects include raising public and provider awareness of racial/ethnic and gender/sexual orientation disparities in care, there needs to be a continuously expanding insurance coverage for minority groups, improve the capacity and number of providers in underserved communities, and increasing the knowledge base on causes and interventions to reduce disparities.

Our model seeks to resolve the issue of disparities in healthcare through giving specific representation of the divide in access to healthcare and the impact that has on the treatment of minorities in healthcare.

This is done by representing several minority groups and displaying the impact that certain factors have in affecting those specific groups.

Our model consists of two distinct agents. Our agents will visually be represented as people and each agent will be representative of a particular group within society. The number of agents will not change over time and the two agents will have different colors.

Also, the agents will visually appear the exact same in the aspect of their size and shape.

The minority group will be represented as yellow and the majority group will be represented as pink.

The agents are also representative of minorities specifically within the United States.

Yellow specifically represents people of color as well as those who identify within the LGBTQ+ spectrum and those who identify as being a woman.

Pink specifically represents those that are not people of color and who identify within the gender binary and who do not identify within the LGBTQ+ spectrum.

The interaction rules that our model follows is that the starting agents are 30 out of 100 and each agent will be affected by a virus in which the rate of infection stimulates health concerns that are going on throughout society. Tolerance comes into play when the recovery rate begins to directly affect the minority group in a negative manner. Tolerance affects the probability of the recovery rate and the recovery rate is the rate in which the individual will either get healthy or stay sick.

Each agent is only affected by the virus and the amount of tolerance, and the agents do not interact with each other.

These interaction rules are based on the real world aspect that when minorities are affected by a certain health concern, those individuals are more likely to not survive or take longer to get healthy.

When setup is selected, the agents are seen in two different colors for now, but the multiple colors should be able to be changed to represent different and more specific groups.

The parameters within our model are tolerance, rate of disease, and dependent recovery rate.

We have 100 agents, ⅔ are majority. Thirty initial sick with small growth rate are to simulate prevalence of disease.

The parameter that is going to be swept is the intersectionality of recovery rate, tolerance, and prevalence among the population.

The rate of disease is the emergent property that is the main aspect we seek to observe and understand.

When the model is running what will be plotted is the percentages of minority and majority healthy, and the minority and majority sick. Also, the model is plotting who is recovering from sickness and who is not.

Ideally with the model being fully run, the model would show that tolerance does affect the recovery rate and prevalence of disease among minority population by clearly showing disparity relative to the majority.

Our hypothesis would be that tolerance would have a large impact on who specifically recovers from the disease and minorities would be the ones most negatively affected.

Originally the model was to show specifically level of tolerance, disease spread, and the presence of certain diseases.

The simulation also would have shown the tolerance of specific doctors to show how preference and bias also affects recovery rate of diseases in minorities and would be an accurate real world situation.

Perhaps the final simulation also would have showed the complexity in intersectionality.

So far, our model is still a work in progress but our ideas and thoughts of how to finish executing the model do imply real world situations.

We had hoped to show that levels of care could largely be impacted by particular aspects of bias and how prevalent that is within the system of healthcare and how damaging that can be.

The idea of our model would prove consistent with the studies that have been done involving our topic.

Understanding that healthcare can be more equitable over all is a aspect that we hope individuals will take from our model.

A restriction to our model includes the fact that intersectionality is not clearly represented and each specific minority is not specifically evaluated. Rather, minorities are all clustered into one singular group. Our group of minorities could further be broken down in order to understand the various aspects of disparities, discrimination, and perhaps even tolerance of certain groups.

Another restriction to our model would be that the rate of disease was not able to be fully represented and was not able to effectively run within our model. If the rate of disease was able to fully be represented, then data would be available to show how certain diseases affect minorities more so than the majority of the population.

Commonwealth Fund. “Minority Americans Lag Behind Whites On Nearly Every Measure Of Health Care Quality.” Commonwealth Fund, 6 Mar. 2002. www.commonwealthfund.org/press-release/2002/minority-americans-lag-behind-whites-nearly-every-measure-health-care-quality.

Paulsen, Emily. “Recognizing, Addressing Unintended Gender Bias in Patient Care.” Duke Health, 14 Jan. 2020. www.physicians.dukehealth.org/articles/recognizing-addressing-unintended-gender-bias-patient-care.

KFF. “Eliminating Racial/Ethnic Disparities in Health Care: What Are the Options?” KFF, 20 Oct. 2008. www.kff.org/racial-equity-and-health-policy/issue-brief/eliminating-racialethnic-disparities-in-health-care-what/.